# Streaming data recovery via Bayesian tensor train decomposition

arXiv: https://arxiv.org/abs/2302.12148 · HTML: https://mlsanity.com/paper/2302.12148/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 37 references that are in the map.
- First posted on arXiv 23 Feb 2023.

## Builds on (most cited first, 7 of 7)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Streaming Variational Bayes](https://mlsanity.com/paper/1307.6769/) (2013)
- [Distributed Flexible Nonlinear Tensor Factorization](https://mlsanity.com/paper/1604.07928/) (2016)
- [High-dimension Tensor Completion via Gradient-based Optimization Under Tensor-train Format](https://mlsanity.com/paper/1804.01983/) (2018)
- [Tensor Completion by Alternating Minimization under the Tensor Train (TT) Model](https://mlsanity.com/paper/1609.05587/) (2016)
- [Streaming data preprocessing via online tensor recovery for large environmental sensor networks](https://mlsanity.com/paper/2109.00596/) (2021)
- [Tensor Train Random Projection](https://mlsanity.com/paper/2010.10797/) (2020)

## Abstract (arXiv)

In this paper, we study a Bayesian tensor train (TT) decomposition method to recover streaming data by approximating the latent structure in high-order streaming data. Drawing on the streaming variational Bayes method, we introduce the TT format into Bayesian tensor decomposition methods for streaming data, and formulate posteriors of TT cores. Thanks to the Bayesian framework of the TT format, the proposed algorithm (SPTT) excels in recovering streaming data with high-order, incomplete, and noisy properties. The experiments in synthetic and real-world datasets show the accuracy of our method compared to state-of-the-art Bayesian tensor decomposition methods for streaming data.